How can I import the MNIST dataset that has been manually downloaded?

Well, the keras.datasets.mnist file is really short. You can manually simulate the same action, that is:

  1. Download a dataset from https://s3.amazonaws.com/img-datasets/mnist.pkl.gz

  2. .

    import gzip
f = gzip.open('mnist.pkl.gz', 'rb')
if sys.version_info < (3,):
    data = cPickle.load(f)
else:
    data = cPickle.load(f, encoding='bytes')
f.close()
(x_train, _), (x_test, _) = data

You do not need additional code for that but can tell load_data to load a local version in the first place:

  1. You can download the file https://s3.amazonaws.com/img-datasets/mnist.npz from another computer with proper (proxy) access (taken from https://github.com/keras-team/keras/blob/master/keras/datasets/mnist.py),
  2. copy it to the the directory ~/.keras/datasets/ (on Linux and macOS)
  3. and run load_data(path='mnist.npz') with the right file name

Keras file is located into a new path in Google Cloud Storage (Before it was in AWS S3):

https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz

When using:

tf.keras.datasets.mnist.load_data()

You can pass a path parameter.

load_data() will call get_file() which takes as parameter fname, if path is a full path and file exists, it will not be downloaded.

Example:

# gsutil cp gs://tensorflow/tf-keras-datasets/mnist.npz /tmp/data/mnist.npz
# python3
>>> import tensorflow as tf
>>> path = '/tmp/data/mnist.npz'
>>> (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data(path)
>>> len(train_images)
>>> 60000

  1. Download file https://s3.amazonaws.com/img-datasets/mnist.npz
  2. Move mnist.npz to .keras/datasets/ directory

  3. Load data

    import keras
from keras.datasets import mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()

Tags:

Keras

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